双极性复发的早期预警信号:调查智能手机数据的关键减速
Vera M Ludwig1, Carl A Bittendorf2, Iris Reinhard3
1Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.
Journal of affective disorders
|July 30, 2025
概括
智能手机数字表型显示为早期检测双相情绪障碍 (BD) 情绪发作提供了希望. 然而,目前的预测准确性尚未在临床上有用,需要进一步研究以改善复发预测.
科学领域:
- 动态系统理论 动态系统理论
- 数字化表型化是指数字化表型化.
- 双极性障碍 (BD) 研究研究
背景情况:
- 早期预警信号 (EWS),如增加的自相关性 (AR) 和变异,可能会在双相情绪障碍 (BD) 中出现情绪发作.
- 基于智能手机的数字表型被探索为一种方法,可以在情绪发作之前检测这些EWS.
- 这项研究研究了被动收集数据在预测抑郁和 (低) 躁狂发作方面的潜力.
研究的目的:
- 为了检查基于智能手机的数字表型化是否可以检测双相情绪障碍 (BD) 情绪发作之前的早期预警信号 (EWS).
- 评估动态系统理论指标 (AR,方差) 的预测能力,这些指标是从情绪发作的数字表型化数据中获得的.
主要方法:
- 分析了智能手机传感器和29名BD患者的一年 (10,587个患者日) 的自我报告数据.
- 包括30次抑郁和20次 (低) 躁狂发作,每天的疾病状况通过每两周一次的专家采访得到证实.
- 从活动,通信和睡眠数据计算AR,方差和移动平均值;用于预测和实用性评估的多层次逻辑模型和ROC曲线.
主要成果:
- 几个参数显著预测了前一集的几周,但没有发现任何一个强大的预测因素.
- 改变的AR和活动量差最好预测躁狂发作;睡眠参数预测躁狂和抑郁的过渡.
- 综合潜伏因素显示出比单个变量更强的预测潜力,但未达到临床效用值.
结论:
- 基于智能手机的数字表型识别显示了在双相情绪障碍 (BD) 中早期检测情绪发作的潜力.
- 目前的预测准确性对于临床效用来说是不够的.
- 未来的研究应该专注于改进参数,采用机器学习和优化分析框架,以有效预测BD复发.
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